Overview

Dataset statistics

Number of variables17
Number of observations1000
Missing cells2741
Missing cells (%)16.1%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory682.9 KiB
Average record size in memory699.3 B

Variable types

Numeric7
Text4
DateTime1
Categorical4
Unsupported1

Alerts

job has 97 (9.7%) missing valuesMissing
postCode has 1000 (100.0%) missing valuesMissing
didPurchase has 292 (29.2%) missing valuesMissing
didRecommend has 544 (54.4%) missing valuesMissing
isUsefull has 801 (80.1%) missing valuesMissing
reviewid is uniformly distributedUniform
reviewid has unique valuesUnique
postCode is an unsupported type, check if it needs cleaning or further analysisUnsupported
ratingShipping has 11 (1.1%) zerosZeros

Reproduction

Analysis started2024-04-30 18:42:46.346018
Analysis finished2024-04-30 18:42:55.491525
Duration9.15 seconds
Software versionydata-profiling vv4.7.0
Download configurationconfig.json

Variables

reviewid
Real number (ℝ)

UNIFORM  UNIQUE 

Distinct1000
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean500.722
Minimum1
Maximum1001
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.9 KiB
2024-04-30T20:42:55.655162image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile50.95
Q1250.75
median500.5
Q3750.25
95-th percentile951.05
Maximum1001
Range1000
Interquartile range (IQR)499.5

Descriptive statistics

Standard deviation289.11888
Coefficient of variation (CV)0.57740399
Kurtosis-1.1993159
Mean500.722
Median Absolute Deviation (MAD)250
Skewness0.0019987283
Sum500722
Variance83589.728
MonotonicityStrictly increasing
2024-04-30T20:42:56.107909image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 1
 
0.1%
672 1
 
0.1%
659 1
 
0.1%
660 1
 
0.1%
661 1
 
0.1%
662 1
 
0.1%
663 1
 
0.1%
664 1
 
0.1%
665 1
 
0.1%
666 1
 
0.1%
Other values (990) 990
99.0%
ValueCountFrequency (%)
1 1
0.1%
2 1
0.1%
3 1
0.1%
4 1
0.1%
5 1
0.1%
6 1
0.1%
7 1
0.1%
8 1
0.1%
9 1
0.1%
10 1
0.1%
ValueCountFrequency (%)
1001 1
0.1%
1000 1
0.1%
999 1
0.1%
998 1
0.1%
997 1
0.1%
996 1
0.1%
995 1
0.1%
994 1
0.1%
993 1
0.1%
992 1
0.1%

productid
Real number (ℝ)

Distinct288
Distinct (%)28.8%
Missing1
Missing (%)0.1%
Infinite0
Infinite (%)0.0%
Mean844.78278
Minimum342
Maximum999
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.9 KiB
2024-04-30T20:42:56.314619image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum342
5-th percentile714
Q1774
median842
Q3922
95-th percentile983.1
Maximum999
Range657
Interquartile range (IQR)148

Descriptive statistics

Standard deviation94.133374
Coefficient of variation (CV)0.11142909
Kurtosis2.5314517
Mean844.78278
Median Absolute Deviation (MAD)76
Skewness-0.7231522
Sum843938
Variance8861.092
MonotonicityNot monotonic
2024-04-30T20:42:56.548952image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
748 10
 
1.0%
785 9
 
0.9%
800 7
 
0.7%
993 7
 
0.7%
817 7
 
0.7%
762 7
 
0.7%
932 7
 
0.7%
804 7
 
0.7%
736 7
 
0.7%
904 7
 
0.7%
Other values (278) 924
92.4%
ValueCountFrequency (%)
342 1
 
0.1%
369 1
 
0.1%
376 1
 
0.1%
385 2
0.2%
394 1
 
0.1%
416 1
 
0.1%
571 1
 
0.1%
645 1
 
0.1%
680 4
0.4%
706 4
0.4%
ValueCountFrequency (%)
999 3
0.3%
998 1
 
0.1%
997 2
 
0.2%
996 3
0.3%
995 4
0.4%
994 3
0.3%
993 7
0.7%
992 1
 
0.1%
991 5
0.5%
990 4
0.4%

ip
Text

Distinct391
Distinct (%)39.1%
Missing1
Missing (%)0.1%
Memory size68.7 KiB
2024-04-30T20:42:56.972656image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Length

Max length15
Median length14
Mean length13.221221
Min length10

Characters and Unicode

Total characters13208
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique97 ?
Unique (%)9.7%

Sample

1st row235.254.248.5
2nd row184.152.249.5
3rd row187.190.241.112
4th row5.140.171.130
5th row178.84.50.74
ValueCountFrequency (%)
84.48.127.221 9
 
0.9%
178.84.50.74 7
 
0.7%
171.155.141.188 7
 
0.7%
4.92.46.52 6
 
0.6%
160.34.193.167 6
 
0.6%
28.188.42.109 6
 
0.6%
176.220.174.95 6
 
0.6%
83.43.1.202 5
 
0.5%
149.47.105.185 5
 
0.5%
198.16.65.6 5
 
0.5%
Other values (381) 937
93.8%
2024-04-30T20:42:57.511922image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
. 2997
22.7%
1 2445
18.5%
2 1647
12.5%
5 915
 
6.9%
4 850
 
6.4%
9 788
 
6.0%
3 754
 
5.7%
7 749
 
5.7%
8 739
 
5.6%
6 663
 
5.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 10211
77.3%
Other Punctuation 2997
 
22.7%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 2445
23.9%
2 1647
16.1%
5 915
 
9.0%
4 850
 
8.3%
9 788
 
7.7%
3 754
 
7.4%
7 749
 
7.3%
8 739
 
7.2%
6 663
 
6.5%
0 661
 
6.5%
Other Punctuation
ValueCountFrequency (%)
. 2997
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 13208
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
. 2997
22.7%
1 2445
18.5%
2 1647
12.5%
5 915
 
6.9%
4 850
 
6.4%
9 788
 
6.0%
3 754
 
5.7%
7 749
 
5.7%
8 739
 
5.6%
6 663
 
5.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 13208
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
. 2997
22.7%
1 2445
18.5%
2 1647
12.5%
5 915
 
6.9%
4 850
 
6.4%
9 788
 
6.0%
3 754
 
5.7%
7 749
 
5.7%
8 739
 
5.6%
6 663
 
5.0%

date
Date

Distinct338
Distinct (%)33.8%
Missing1
Missing (%)0.1%
Memory size7.9 KiB
Minimum2011-01-01 00:00:00
Maximum2011-12-30 00:00:00
2024-04-30T20:42:57.784855image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-30T20:42:58.044175image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

ratingWebsite
Real number (ℝ)

Distinct101
Distinct (%)10.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.9669
Minimum0
Maximum10
Zeros6
Zeros (%)0.6%
Negative0
Negative (%)0.0%
Memory size7.9 KiB
2024-04-30T20:42:58.279205image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.6
Q12.6
median4.7
Q37.5
95-th percentile9.405
Maximum10
Range10
Interquartile range (IQR)4.9

Descriptive statistics

Standard deviation2.867727
Coefficient of variation (CV)0.57736758
Kurtosis-1.1891777
Mean4.9669
Median Absolute Deviation (MAD)2.4
Skewness0.04475262
Sum4966.9
Variance8.2238582
MonotonicityNot monotonic
2024-04-30T20:42:58.499624image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
6.3 18
 
1.8%
3.8 16
 
1.6%
4 15
 
1.5%
0.7 15
 
1.5%
3.6 15
 
1.5%
8.7 15
 
1.5%
9.1 15
 
1.5%
4.5 15
 
1.5%
4.6 15
 
1.5%
3.7 14
 
1.4%
Other values (91) 847
84.7%
ValueCountFrequency (%)
0 6
 
0.6%
0.1 8
0.8%
0.2 5
 
0.5%
0.3 13
1.3%
0.4 7
0.7%
0.5 10
1.0%
0.6 7
0.7%
0.7 15
1.5%
0.8 10
1.0%
0.9 13
1.3%
ValueCountFrequency (%)
10 4
 
0.4%
9.9 12
1.2%
9.8 7
0.7%
9.7 12
1.2%
9.6 7
0.7%
9.5 8
0.8%
9.4 8
0.8%
9.3 10
1.0%
9.2 12
1.2%
9.1 15
1.5%

ratingShipping
Real number (ℝ)

ZEROS 

Distinct101
Distinct (%)10.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.1123
Minimum0
Maximum10
Zeros11
Zeros (%)1.1%
Negative0
Negative (%)0.0%
Memory size7.9 KiB
2024-04-30T20:42:58.724319image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.395
Q12.6
median5.3
Q37.6
95-th percentile9.5
Maximum10
Range10
Interquartile range (IQR)5

Descriptive statistics

Standard deviation2.8966307
Coefficient of variation (CV)0.56660029
Kurtosis-1.1607925
Mean5.1123
Median Absolute Deviation (MAD)2.5
Skewness-0.085864145
Sum5112.3
Variance8.3904692
MonotonicityNot monotonic
2024-04-30T20:42:58.963745image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
6.6 18
 
1.8%
5.9 17
 
1.7%
7.9 16
 
1.6%
3.3 16
 
1.6%
8.8 16
 
1.6%
4.1 15
 
1.5%
0.1 15
 
1.5%
6.5 15
 
1.5%
5.6 14
 
1.4%
8 14
 
1.4%
Other values (91) 844
84.4%
ValueCountFrequency (%)
0 11
1.1%
0.1 15
1.5%
0.2 12
1.2%
0.3 12
1.2%
0.4 10
1.0%
0.5 9
0.9%
0.6 5
 
0.5%
0.7 10
1.0%
0.8 6
 
0.6%
0.9 5
 
0.5%
ValueCountFrequency (%)
10 3
 
0.3%
9.9 10
1.0%
9.8 12
1.2%
9.7 11
1.1%
9.6 8
0.8%
9.5 10
1.0%
9.4 13
1.3%
9.3 12
1.2%
9.2 13
1.3%
9.1 8
0.8%

ratingProduct
Real number (ℝ)

Distinct91
Distinct (%)9.1%
Missing1
Missing (%)0.1%
Infinite0
Infinite (%)0.0%
Mean5.4437437
Minimum1
Maximum10
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.9 KiB
2024-04-30T20:42:59.174710image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1.4
Q13.2
median5.5
Q37.8000002
95-th percentile9.3999996
Maximum10
Range9
Interquartile range (IQR)4.6000002

Descriptive statistics

Standard deviation2.6134496
Coefficient of variation (CV)0.48008314
Kurtosis-1.2211017
Mean5.4437437
Median Absolute Deviation (MAD)2.3000002
Skewness-0.027954799
Sum5438.3
Variance6.8301186
MonotonicityNot monotonic
2024-04-30T20:42:59.388301image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
5.1999998 22
 
2.2%
1.5 18
 
1.8%
2 18
 
1.8%
5.5 18
 
1.8%
8 17
 
1.7%
8.3000002 16
 
1.6%
1.1 16
 
1.6%
7.8000002 16
 
1.6%
9.1999998 16
 
1.6%
7.1999998 16
 
1.6%
Other values (81) 826
82.6%
ValueCountFrequency (%)
1 9
0.9%
1.1 16
1.6%
1.2 9
0.9%
1.3 12
1.2%
1.4 7
 
0.7%
1.5 18
1.8%
1.6 10
1.0%
1.7 15
1.5%
1.8 14
1.4%
1.9 8
0.8%
ValueCountFrequency (%)
10 5
 
0.5%
9.8999996 12
1.2%
9.8000002 9
0.9%
9.6999998 9
0.9%
9.6000004 8
0.8%
9.5 6
 
0.6%
9.3999996 9
0.9%
9.3000002 4
 
0.4%
9.1999998 16
1.6%
9.1000004 15
1.5%

ratingOverall
Real number (ℝ)

Distinct84
Distinct (%)8.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.1734
Minimum0.7
Maximum9.8
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.9 KiB
2024-04-30T20:42:59.600446image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0.7
5-th percentile2.6
Q14
median5.2
Q36.3
95-th percentile7.8
Maximum9.8
Range9.1
Interquartile range (IQR)2.3

Descriptive statistics

Standard deviation1.5813833
Coefficient of variation (CV)0.30567583
Kurtosis-0.29879677
Mean5.1734
Median Absolute Deviation (MAD)1.1
Skewness-0.028353539
Sum5173.4
Variance2.5007732
MonotonicityNot monotonic
2024-04-30T20:42:59.808832image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
5.2 30
 
3.0%
6.4 28
 
2.8%
4 27
 
2.7%
4.7 27
 
2.7%
5.5 26
 
2.6%
5.4 26
 
2.6%
5.8 25
 
2.5%
4.8 25
 
2.5%
5.9 25
 
2.5%
6.1 25
 
2.5%
Other values (74) 736
73.6%
ValueCountFrequency (%)
0.7 1
 
0.1%
0.8 1
 
0.1%
1 1
 
0.1%
1.3 1
 
0.1%
1.4 1
 
0.1%
1.5 2
0.2%
1.6 3
0.3%
1.7 4
0.4%
1.8 4
0.4%
1.9 4
0.4%
ValueCountFrequency (%)
9.8 1
 
0.1%
9.6 1
 
0.1%
9.4 1
 
0.1%
9.3 1
 
0.1%
9 2
0.2%
8.9 1
 
0.1%
8.8 3
0.3%
8.7 3
0.3%
8.6 2
0.2%
8.5 4
0.4%

gender
Categorical

Distinct2
Distinct (%)0.2%
Missing1
Missing (%)0.1%
Memory size60.7 KiB
Female
507 
Male
492 

Length

Max length6
Median length6
Mean length5.015015
Min length4

Characters and Unicode

Total characters5010
Distinct characters6
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowFemale
2nd rowMale
3rd rowMale
4th rowMale
5th rowFemale

Common Values

ValueCountFrequency (%)
Female 507
50.7%
Male 492
49.2%
(Missing) 1
 
0.1%

Length

2024-04-30T20:43:00.018461image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-30T20:43:00.176395image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
female 507
50.8%
male 492
49.2%

Most occurring characters

ValueCountFrequency (%)
e 1506
30.1%
a 999
19.9%
l 999
19.9%
F 507
 
10.1%
m 507
 
10.1%
M 492
 
9.8%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 4011
80.1%
Uppercase Letter 999
 
19.9%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 1506
37.5%
a 999
24.9%
l 999
24.9%
m 507
 
12.6%
Uppercase Letter
ValueCountFrequency (%)
F 507
50.8%
M 492
49.2%

Most occurring scripts

ValueCountFrequency (%)
Latin 5010
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 1506
30.1%
a 999
19.9%
l 999
19.9%
F 507
 
10.1%
m 507
 
10.1%
M 492
 
9.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 5010
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 1506
30.1%
a 999
19.9%
l 999
19.9%
F 507
 
10.1%
m 507
 
10.1%
M 492
 
9.8%

email
Text

Distinct999
Distinct (%)100.0%
Missing1
Missing (%)0.1%
Memory size77.2 KiB
2024-04-30T20:43:00.448590image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Length

Max length37
Median length33
Mean length21.976977
Min length14

Characters and Unicode

Total characters21955
Distinct characters39
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique999 ?
Unique (%)100.0%

Sample

1st rowcmullarkey0@jimdo.com
2nd rowbhobben1@tamu.edu
3rd rowringlesfield2@sourceforge.net
4th rowcgrogor3@wikipedia.org
5th rowtcashell4@ocn.ne.jp
ValueCountFrequency (%)
cmullarkey0@jimdo.com 1
 
0.1%
lkleeweind@ucoz.ru 1
 
0.1%
pwahlbergu@elegantthemes.com 1
 
0.1%
ebodleyt@skype.com 1
 
0.1%
ringlesfield2@sourceforge.net 1
 
0.1%
cgrogor3@wikipedia.org 1
 
0.1%
tcashell4@ocn.ne.jp 1
 
0.1%
vmockler5@chicagotribune.com 1
 
0.1%
mpadula6@sourceforge.net 1
 
0.1%
welleray7@uiuc.edu 1
 
0.1%
Other values (989) 989
99.0%
2024-04-30T20:43:00.988793image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
o 1909
 
8.7%
e 1744
 
7.9%
c 1343
 
6.1%
a 1266
 
5.8%
m 1247
 
5.7%
r 1164
 
5.3%
. 1093
 
5.0%
i 1087
 
5.0%
n 1030
 
4.7%
@ 999
 
4.6%
Other values (29) 9073
41.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 19173
87.3%
Other Punctuation 2092
 
9.5%
Decimal Number 669
 
3.0%
Dash Punctuation 21
 
0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
o 1909
 
10.0%
e 1744
 
9.1%
c 1343
 
7.0%
a 1266
 
6.6%
m 1247
 
6.5%
r 1164
 
6.1%
i 1087
 
5.7%
n 1030
 
5.4%
l 990
 
5.2%
s 961
 
5.0%
Other values (16) 6432
33.5%
Decimal Number
ValueCountFrequency (%)
1 79
11.8%
3 77
11.5%
2 76
11.4%
4 73
10.9%
8 71
10.6%
6 71
10.6%
9 64
9.6%
7 64
9.6%
5 64
9.6%
0 30
 
4.5%
Other Punctuation
ValueCountFrequency (%)
. 1093
52.2%
@ 999
47.8%
Dash Punctuation
ValueCountFrequency (%)
- 21
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 19173
87.3%
Common 2782
 
12.7%

Most frequent character per script

Latin
ValueCountFrequency (%)
o 1909
 
10.0%
e 1744
 
9.1%
c 1343
 
7.0%
a 1266
 
6.6%
m 1247
 
6.5%
r 1164
 
6.1%
i 1087
 
5.7%
n 1030
 
5.4%
l 990
 
5.2%
s 961
 
5.0%
Other values (16) 6432
33.5%
Common
ValueCountFrequency (%)
. 1093
39.3%
@ 999
35.9%
1 79
 
2.8%
3 77
 
2.8%
2 76
 
2.7%
4 73
 
2.6%
8 71
 
2.6%
6 71
 
2.6%
9 64
 
2.3%
7 64
 
2.3%
Other values (3) 115
 
4.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 21955
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
o 1909
 
8.7%
e 1744
 
7.9%
c 1343
 
6.1%
a 1266
 
5.8%
m 1247
 
5.7%
r 1164
 
5.3%
. 1093
 
5.0%
i 1087
 
5.0%
n 1030
 
4.7%
@ 999
 
4.6%
Other values (29) 9073
41.3%

job
Text

MISSING 

Distinct179
Distinct (%)19.8%
Missing97
Missing (%)9.7%
Memory size69.8 KiB
2024-04-30T20:43:01.321794image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Length

Max length36
Median length25
Mean length18.604651
Min length5

Characters and Unicode

Total characters16800
Distinct characters47
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique24 ?
Unique (%)2.7%

Sample

1st rowSafety Technician IV
2nd rowCivil Engineer
3rd rowResearch Associate
4th rowPayment Adjustment Coordinator
5th rowEnvironmental Specialist
ValueCountFrequency (%)
engineer 122
 
5.7%
assistant 94
 
4.4%
manager 84
 
3.9%
analyst 66
 
3.1%
ii 65
 
3.0%
iii 61
 
2.8%
specialist 57
 
2.6%
systems 51
 
2.4%
i 50
 
2.3%
iv 49
 
2.3%
Other values (117) 1452
67.5%
2024-04-30T20:43:01.873676image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e 1627
 
9.7%
t 1305
 
7.8%
a 1282
 
7.6%
i 1273
 
7.6%
n 1256
 
7.5%
1248
 
7.4%
r 1082
 
6.4%
s 1043
 
6.2%
c 732
 
4.4%
o 705
 
4.2%
Other values (37) 5247
31.2%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 13092
77.9%
Uppercase Letter 2450
 
14.6%
Space Separator 1248
 
7.4%
Other Punctuation 10
 
0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 1627
12.4%
t 1305
10.0%
a 1282
9.8%
i 1273
9.7%
n 1256
9.6%
r 1082
8.3%
s 1043
8.0%
c 732
 
5.6%
o 705
 
5.4%
l 525
 
4.0%
Other values (14) 2262
17.3%
Uppercase Letter
ValueCountFrequency (%)
I 445
18.2%
A 358
14.6%
S 315
12.9%
E 187
7.6%
P 174
 
7.1%
M 141
 
5.8%
C 134
 
5.5%
D 123
 
5.0%
V 91
 
3.7%
T 88
 
3.6%
Other values (11) 394
16.1%
Space Separator
ValueCountFrequency (%)
1248
100.0%
Other Punctuation
ValueCountFrequency (%)
/ 10
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 15542
92.5%
Common 1258
 
7.5%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 1627
 
10.5%
t 1305
 
8.4%
a 1282
 
8.2%
i 1273
 
8.2%
n 1256
 
8.1%
r 1082
 
7.0%
s 1043
 
6.7%
c 732
 
4.7%
o 705
 
4.5%
l 525
 
3.4%
Other values (35) 4712
30.3%
Common
ValueCountFrequency (%)
1248
99.2%
/ 10
 
0.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 16800
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 1627
 
9.7%
t 1305
 
7.8%
a 1282
 
7.6%
i 1273
 
7.6%
n 1256
 
7.5%
1248
 
7.4%
r 1082
 
6.4%
s 1043
 
6.2%
c 732
 
4.4%
o 705
 
4.2%
Other values (37) 5247
31.2%

postCode
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing1000
Missing (%)100.0%
Memory size7.9 KiB

source
Categorical

Distinct8
Distinct (%)0.8%
Missing1
Missing (%)0.1%
Memory size61.4 KiB
Other
366 
Bing
116 
YouTube
99 
Amazon
92 
Twitter
91 
Other values (3)
235 

Length

Max length8
Median length7
Mean length5.7237237
Min length4

Characters and Unicode

Total characters5718
Distinct characters24
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowTwitter
2nd rowTwitter
3rd rowOther
4th rowOther
5th rowTikTok

Common Values

ValueCountFrequency (%)
Other 366
36.6%
Bing 116
 
11.6%
YouTube 99
 
9.9%
Amazon 92
 
9.2%
Twitter 91
 
9.1%
Google 88
 
8.8%
TikTok 81
 
8.1%
Facebook 66
 
6.6%
(Missing) 1
 
0.1%

Length

2024-04-30T20:43:02.139563image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-30T20:43:02.332472image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
other 366
36.6%
bing 116
 
11.6%
youtube 99
 
9.9%
amazon 92
 
9.2%
twitter 91
 
9.1%
google 88
 
8.8%
tiktok 81
 
8.1%
facebook 66
 
6.6%

Most occurring characters

ValueCountFrequency (%)
e 710
12.4%
o 580
 
10.1%
t 548
 
9.6%
r 457
 
8.0%
O 366
 
6.4%
h 366
 
6.4%
T 352
 
6.2%
i 288
 
5.0%
k 228
 
4.0%
n 208
 
3.6%
Other values (14) 1615
28.2%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 4539
79.4%
Uppercase Letter 1179
 
20.6%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 710
15.6%
o 580
12.8%
t 548
12.1%
r 457
10.1%
h 366
8.1%
i 288
 
6.3%
k 228
 
5.0%
n 208
 
4.6%
g 204
 
4.5%
u 198
 
4.4%
Other values (7) 752
16.6%
Uppercase Letter
ValueCountFrequency (%)
O 366
31.0%
T 352
29.9%
B 116
 
9.8%
Y 99
 
8.4%
A 92
 
7.8%
G 88
 
7.5%
F 66
 
5.6%

Most occurring scripts

ValueCountFrequency (%)
Latin 5718
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 710
12.4%
o 580
 
10.1%
t 548
 
9.6%
r 457
 
8.0%
O 366
 
6.4%
h 366
 
6.4%
T 352
 
6.2%
i 288
 
5.0%
k 228
 
4.0%
n 208
 
3.6%
Other values (14) 1615
28.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 5718
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 710
12.4%
o 580
 
10.1%
t 548
 
9.6%
r 457
 
8.0%
O 366
 
6.4%
h 366
 
6.4%
T 352
 
6.2%
i 288
 
5.0%
k 228
 
4.0%
n 208
 
3.6%
Other values (14) 1615
28.2%

didPurchase
Categorical

MISSING 

Distinct2
Distinct (%)0.3%
Missing292
Missing (%)29.2%
Memory size59.3 KiB
0
412 
TRUE
296 

Length

Max length4
Median length1
Mean length2.2542373
Min length1

Characters and Unicode

Total characters1596
Distinct characters5
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowTRUE
2nd rowTRUE
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 412
41.2%
TRUE 296
29.6%
(Missing) 292
29.2%

Length

2024-04-30T20:43:02.561615image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-30T20:43:02.726280image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
0 412
58.2%
true 296
41.8%

Most occurring characters

ValueCountFrequency (%)
0 412
25.8%
T 296
18.5%
R 296
18.5%
U 296
18.5%
E 296
18.5%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 1184
74.2%
Decimal Number 412
 
25.8%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
T 296
25.0%
R 296
25.0%
U 296
25.0%
E 296
25.0%
Decimal Number
ValueCountFrequency (%)
0 412
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 1184
74.2%
Common 412
 
25.8%

Most frequent character per script

Latin
ValueCountFrequency (%)
T 296
25.0%
R 296
25.0%
U 296
25.0%
E 296
25.0%
Common
ValueCountFrequency (%)
0 412
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1596
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 412
25.8%
T 296
18.5%
R 296
18.5%
U 296
18.5%
E 296
18.5%

didRecommend
Categorical

MISSING 

Distinct2
Distinct (%)0.4%
Missing544
Missing (%)54.4%
Memory size60.8 KiB
1.0
397 
0.0
59 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters1368
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row1.0
3rd row1.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.0 397
39.7%
0.0 59
 
5.9%
(Missing) 544
54.4%

Length

2024-04-30T20:43:02.883471image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-30T20:43:03.018590image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
1.0 397
87.1%
0.0 59
 
12.9%

Most occurring characters

ValueCountFrequency (%)
0 515
37.6%
. 456
33.3%
1 397
29.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 912
66.7%
Other Punctuation 456
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 515
56.5%
1 397
43.5%
Other Punctuation
ValueCountFrequency (%)
. 456
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1368
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 515
37.6%
. 456
33.3%
1 397
29.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1368
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 515
37.6%
. 456
33.3%
1 397
29.0%

isUsefull
Real number (ℝ)

MISSING 

Distinct26
Distinct (%)13.1%
Missing801
Missing (%)80.1%
Infinite0
Infinite (%)0.0%
Mean11.763819
Minimum0
Maximum25
Zeros4
Zeros (%)0.4%
Negative0
Negative (%)0.0%
Memory size7.9 KiB
2024-04-30T20:43:03.168581image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q15
median11
Q318
95-th percentile23
Maximum25
Range25
Interquartile range (IQR)13

Descriptive statistics

Standard deviation7.5531449
Coefficient of variation (CV)0.64206571
Kurtosis-1.3314302
Mean11.763819
Median Absolute Deviation (MAD)7
Skewness0.10979186
Sum2341
Variance57.049997
MonotonicityNot monotonic
2024-04-30T20:43:03.357287image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=26)
ValueCountFrequency (%)
7 14
 
1.4%
1 13
 
1.3%
23 12
 
1.2%
2 11
 
1.1%
17 11
 
1.1%
9 10
 
1.0%
5 10
 
1.0%
8 9
 
0.9%
4 9
 
0.9%
18 8
 
0.8%
Other values (16) 92
 
9.2%
(Missing) 801
80.1%
ValueCountFrequency (%)
0 4
 
0.4%
1 13
1.3%
2 11
1.1%
3 8
0.8%
4 9
0.9%
5 10
1.0%
6 4
 
0.4%
7 14
1.4%
8 9
0.9%
9 10
1.0%
ValueCountFrequency (%)
25 2
 
0.2%
24 7
0.7%
23 12
1.2%
22 6
0.6%
21 7
0.7%
20 8
0.8%
19 7
0.7%
18 8
0.8%
17 11
1.1%
16 7
0.7%
Distinct420
Distinct (%)42.0%
Missing0
Missing (%)0.0%
Memory size160.7 KiB
2024-04-30T20:43:03.654963image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Length

Max length244
Median length133
Mean length107.449
Min length47

Characters and Unicode

Total characters107449
Distinct characters72
Distinct categories10 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique116 ?
Unique (%)11.6%

Sample

1st rowMozilla/5.0 (Windows NT 6.1; rv:12.0) Gecko/20120403211507 Firefox/12.0
2nd rowMozilla/5.0 (Windows NT 6.2; WOW64) AppleWebKit/535.11 (KHTML, like Gecko) Chrome/17.0.963.66 Safari/535.11
3rd rowMozilla/5.0 (Windows NT 5.1) AppleWebKit/534.24 (KHTML, like Gecko) Chrome/11.0.700.3 Safari/534.24
4th rowMozilla/5.0 (X11; Linux x86_64; rv:28.0) Gecko/20100101 Firefox/28.0
5th rowMozilla/5.0 (Windows NT 6.2; WOW64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/27.0.1453.93 Safari/537.36
ValueCountFrequency (%)
mozilla/5.0 999
 
8.7%
like 858
 
7.5%
gecko 841
 
7.3%
khtml 831
 
7.2%
windows 581
 
5.1%
nt 471
 
4.1%
os 314
 
2.7%
mac 287
 
2.5%
x 287
 
2.5%
macintosh 260
 
2.3%
Other values (524) 5740
50.0%
2024-04-30T20:43:04.375336image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
10469
 
9.7%
. 6358
 
5.9%
i 5193
 
4.8%
e 5051
 
4.7%
1 4226
 
3.9%
o 4023
 
3.7%
5 4018
 
3.7%
0 4006
 
3.7%
l 3997
 
3.7%
/ 3994
 
3.7%
Other values (62) 56114
52.2%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 40628
37.8%
Decimal Number 23022
21.4%
Uppercase Letter 15792
 
14.7%
Other Punctuation 12905
 
12.0%
Space Separator 10469
 
9.7%
Close Punctuation 1837
 
1.7%
Open Punctuation 1837
 
1.7%
Connector Punctuation 664
 
0.6%
Dash Punctuation 258
 
0.2%
Math Symbol 37
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
i 5193
12.8%
e 5051
12.4%
o 4023
9.9%
l 3997
9.8%
a 3313
 
8.2%
r 2166
 
5.3%
k 1882
 
4.6%
n 1783
 
4.4%
p 1713
 
4.2%
c 1610
 
4.0%
Other values (16) 9897
24.4%
Uppercase Letter
ValueCountFrequency (%)
M 2410
15.3%
W 1713
10.8%
K 1665
10.5%
T 1331
8.4%
S 1278
8.1%
L 1037
 
6.6%
G 1016
 
6.4%
A 855
 
5.4%
H 838
 
5.3%
C 787
 
5.0%
Other values (14) 2862
18.1%
Decimal Number
ValueCountFrequency (%)
1 4226
18.4%
5 4018
17.5%
0 4006
17.4%
3 2981
12.9%
6 2075
9.0%
2 1861
8.1%
4 1599
 
6.9%
7 1019
 
4.4%
8 762
 
3.3%
9 475
 
2.1%
Other Punctuation
ValueCountFrequency (%)
. 6358
49.3%
/ 3994
30.9%
; 1549
 
12.0%
, 828
 
6.4%
: 174
 
1.3%
! 2
 
< 0.1%
Space Separator
ValueCountFrequency (%)
10469
100.0%
Close Punctuation
ValueCountFrequency (%)
) 1837
100.0%
Open Punctuation
ValueCountFrequency (%)
( 1837
100.0%
Connector Punctuation
ValueCountFrequency (%)
_ 664
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 258
100.0%
Math Symbol
ValueCountFrequency (%)
+ 37
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 56420
52.5%
Common 51029
47.5%

Most frequent character per script

Latin
ValueCountFrequency (%)
i 5193
 
9.2%
e 5051
 
9.0%
o 4023
 
7.1%
l 3997
 
7.1%
a 3313
 
5.9%
M 2410
 
4.3%
r 2166
 
3.8%
k 1882
 
3.3%
n 1783
 
3.2%
p 1713
 
3.0%
Other values (40) 24889
44.1%
Common
ValueCountFrequency (%)
10469
20.5%
. 6358
12.5%
1 4226
8.3%
5 4018
 
7.9%
0 4006
 
7.9%
/ 3994
 
7.8%
3 2981
 
5.8%
6 2075
 
4.1%
2 1861
 
3.6%
) 1837
 
3.6%
Other values (12) 9204
18.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 107449
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
10469
 
9.7%
. 6358
 
5.9%
i 5193
 
4.8%
e 5051
 
4.7%
1 4226
 
3.9%
o 4023
 
3.7%
5 4018
 
3.7%
0 4006
 
3.7%
l 3997
 
3.7%
/ 3994
 
3.7%
Other values (62) 56114
52.2%

Interactions

2024-04-30T20:42:53.805588image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-30T20:42:47.172045image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-30T20:42:48.331521image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-30T20:42:49.467754image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-30T20:42:50.596661image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-30T20:42:51.705728image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-30T20:42:52.767820image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-30T20:42:53.937361image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-30T20:42:47.341386image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-30T20:42:48.506212image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-30T20:42:49.630590image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-30T20:42:50.765742image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-30T20:42:51.865546image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-30T20:42:52.918424image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-30T20:42:54.079273image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-30T20:42:47.512163image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-30T20:42:48.672170image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-30T20:42:49.797713image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-30T20:42:50.929224image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-30T20:42:52.025437image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-30T20:42:53.077546image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-30T20:42:54.214179image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-30T20:42:47.670267image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-30T20:42:48.836165image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-30T20:42:49.963911image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-30T20:42:51.080777image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-30T20:42:52.179694image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-30T20:42:53.230722image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-30T20:42:54.349077image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-30T20:42:47.833693image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-30T20:42:49.000893image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-30T20:42:50.127485image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-30T20:42:51.235450image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-30T20:42:52.335431image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-30T20:42:53.384321image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-30T20:42:54.476774image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-30T20:42:47.992035image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-30T20:42:49.157762image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-30T20:42:50.280449image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-30T20:42:51.385891image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-30T20:42:52.488637image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-30T20:42:53.531012image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-30T20:42:54.605950image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-30T20:42:48.163863image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-30T20:42:49.321278image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-30T20:42:50.445148image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-30T20:42:51.554187image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-30T20:42:52.636839image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-30T20:42:53.673369image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Missing values

2024-04-30T20:42:54.831248image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
A simple visualization of nullity by column.
2024-04-30T20:42:55.187643image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

reviewidproductidipdateratingWebsiteratingShippingratingProductratingOverallgenderemailjobpostCodesourcedidPurchasedidRecommendisUsefulluserAgent
01748.0235.254.248.510/08/20113.75.97.65.7Femalecmullarkey0@jimdo.comSafety Technician IVNaNTwitterTRUE1.0NaNMozilla/5.0 (Windows NT 6.1; rv:12.0) Gecko/20120403211507 Firefox/12.0
12804.0184.152.249.510/11/20112.78.21.14.0Malebhobben1@tamu.eduCivil EngineerNaNTwitterTRUENaN18.0Mozilla/5.0 (Windows NT 6.2; WOW64) AppleWebKit/535.11 (KHTML, like Gecko) Chrome/17.0.963.66 Safari/535.11
23781.0187.190.241.1128/05/20110.99.18.56.2Maleringlesfield2@sourceforge.netResearch AssociateNaNOther01.0NaNMozilla/5.0 (Windows NT 5.1) AppleWebKit/534.24 (KHTML, like Gecko) Chrome/11.0.700.3 Safari/534.24
34779.05.140.171.13010/23/20111.65.24.53.8Malecgrogor3@wikipedia.orgNaNNaNOther0NaNNaNMozilla/5.0 (X11; Linux x86_64; rv:28.0) Gecko/20100101 Firefox/28.0
45835.0178.84.50.741/14/20119.41.37.86.2Femaletcashell4@ocn.ne.jpPayment Adjustment CoordinatorNaNTikTok01.0NaNMozilla/5.0 (Windows NT 6.2; WOW64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/27.0.1453.93 Safari/537.36
56941.083.43.1.2023/26/20114.32.91.12.8Femalevmockler5@chicagotribune.comEnvironmental SpecialistNaNOtherNaNNaN2.0Mozilla/5.0 (Windows NT 5.1) AppleWebKit/535.1 (KHTML, like Gecko) Chrome/14.0.813.0 Safari/535.1
67890.0116.167.31.948/29/20116.64.54.65.2Malempadula6@sourceforge.netNaNNaNOther0NaNNaNMozilla/5.0 (Windows NT 6.2; rv:22.0) Gecko/20130405 Firefox/22.0
78938.0102.142.18.1004/03/20111.26.15.54.3Malewelleray7@uiuc.eduNaNNaNOther0NaNNaNMozilla/5.0 (Windows NT 6.1; WOW64; rv:23.0) Gecko/20130406 Firefox/23.0
89831.09.205.252.305/24/20110.79.09.26.3Femalerdanielsen8@upenn.eduRecruiting ManagerNaNGoogle01.01.0Mozilla/5.0 (Macintosh; U; PPC Mac OS X 10_4_11; tr) AppleWebKit/528.4+ (KHTML, like Gecko) Version/4.0dp1 Safari/526.11.2
910852.04.92.46.523/29/20115.28.47.26.9Femaledeastridge9@weather.comAccountant IIINaNBingTRUE1.0NaNMozilla/5.0 (Windows NT 6.2; Win64; x64; rv:16.0.1) Gecko/20121011 Firefox/21.0.1
reviewidproductidipdateratingWebsiteratingShippingratingProductratingOverallgenderemailjobpostCodesourcedidPurchasedidRecommendisUsefulluserAgent
990992785.0142.85.186.1933/30/20118.74.55.36.2Maledfaasrj@bbc.co.ukPhysical Therapy AssistantNaNOtherTRUENaNNaNMozilla/5.0 (X11; Linux i686; rv:21.0) Gecko/20100101 Firefox/21.0
991993807.0180.255.115.9712/19/20112.81.98.74.5Femalecdunkerleyrk@webmd.comEditorNaNOtherNaNNaN21.0Mozilla/5.0 (Windows; U; Windows NT 5.1; ru-RU) AppleWebKit/533.18.1 (KHTML, like Gecko) Version/5.0.2 Safari/533.18.5
992994847.0179.189.57.1565/08/20113.42.18.84.8Femalegtomasinorl@networksolutions.comTechnical WriterNaNTikTokNaN1.011.0Mozilla/5.0 (Macintosh; Intel Mac OS X 10_9_3) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/35.0.1916.47 Safari/537.36
993995916.042.245.196.863/07/20119.13.31.74.7Malembartellrm@cbslocal.comWeb Developer IVNaNOtherTRUENaNNaNMozilla/5.0 (Windows NT 6.2; WOW64) AppleWebKit/537.13 (KHTML, like Gecko) Chrome/24.0.1290.1 Safari/537.13
994996806.065.32.13.18/19/20119.82.94.65.8Femalehmccartrn@admin.chGeneral ManagerNaNFacebook0NaNNaNMozilla/5.0 (Windows NT 6.1; en-US) AppleWebKit/534.30 (KHTML, like Gecko) Chrome/12.0.750.0 Safari/534.30
995997917.041.184.101.238/01/20113.29.72.95.3Femalelhuchotro@twitpic.comComputer Systems Analyst IVNaNBingNaN0.0NaNMozilla/5.0 (X11; U; Linux i686; en-US; rv:1.9.1.16) Gecko/20120421 Firefox/11.0
996998754.0197.50.161.22512/20/20118.41.76.35.5Malelbilofskyrp@1688.comNaNNaNTwitterNaN1.0NaNMozilla/5.0 (Macintosh; U; PPC Mac OS X 10.5; en-US; rv:1.9.1b3pre) Gecko/20081212 Mozilla/5.0 (Windows; U; Windows NT 5.1; en) AppleWebKit/526.9 (KHTML, like Gecko) Version/4.0dp1 Safari/526.8
997999904.0245.91.225.1262/12/20112.91.42.02.1Malebwogdonrq@hp.comSystems Administrator IVNaNFacebook0NaNNaNMozilla/5.0 (X11; Linux x86_64) AppleWebKit/535.1 (KHTML, like Gecko) Chrome/13.0.782.220 Safari/535.1
9981000962.086.147.224.1175/16/20114.28.74.65.8Femalepkamanrr@ox.ac.ukPhysical Therapy AssistantNaNYouTubeTRUENaNNaNMozilla/5.0 (Macintosh; U; Intel Mac OS X 10_6_3; zh-cn) AppleWebKit/533.16 (KHTML, like Gecko) Version/5.0 Safari/533.16
9991001NaNNaNNaN0.31.3NaN0.8NaNNaNNurse PracticionerNaNNaNNaNNaNNaNMozilla/5.0 (Windows NT 5.0; rv:21.0) Gecko/20100101 Firefox/21.0